Devices, methods, and systems for occupancy detection

ABSTRACT

Devices, methods, and systems for occupancy detection are described herein. One device embodiment includes a memory and a processor coupled to the memory. The processor is configured to execute executable instructions stored in the memory to determine energy consumption data associated with a structure at a point in time, and determine whether the structure is occupied at the point in time based, at least in part, on the determined energy consumption data.

This is a continuation application of U.S. patent application Ser. No. 13/192,096, filed Jul. 27, 2011, and entitled “DEVICES, METHODS, AND SYSTEMS FOR OCCUPANCY DETECTION”, which is incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to devices, methods, and systems for occupancy detection.

BACKGROUND

Occupancy detection (e.g., determining whether a structure is occupied by one or more individuals) can be an important part of energy management and/or energy cost savings. For example, the energy management settings of an area in a structure (e.g., a room in a house and/or business) can be adjusted based on whether or not an individual(s) is presently located in the area, thereby reducing and/or eliminating the waste of energy resulting from heating and/or cooling unoccupied areas. As an additional example, appliances located in an area can be turned off or energy consumption reduced if an individual is not presently located in the area, thereby reducing and/or eliminating the waste of energy resulting from running appliances while the area is unoccupied.

There are many different approaches for detecting occupancy. For example, cameras and/or motion detectors, such as, for instance, passive infrared (PIR) detectors, can be used as occupancy sensors. However, cameras and motion detectors, as well as other occupancy sensing devices, may be impractical due to high cost and/or complications associated with their installation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for occupancy detection in accordance with one or more embodiments of the present disclosure.

FIG. 2 illustrates a method for occupancy detection in accordance with one or more embodiments of the present disclosure.

DETAILED DESCRIPTION

Devices, methods, and systems for occupancy detection are described herein. One or more device embodiments include a memory and a processor coupled to the memory. The processor is configured to execute executable instructions stored in the memory to determine energy consumption data associated with a structure at a point in time, and determine whether the structure is occupied at the point in time based, at least in part, on the determined energy consumption data.

Devices, methods, and/or systems for detecting occupancy in accordance with one or more embodiments of the present disclosure can be integrated with a structure's existing power infrastructure, thereby reducing the cost and/or complications associated with their installation. Additionally, embodiments of the present disclosure can determine a predicted occupancy for a structure based on historical energy consumption data for the structure. Embodiments of the present disclosure can receive information from sources outside the structure, including, for example, a GPS location of a user device, to determine whether the structure is occupied, and further, at what time it will or will not be occupied.

In the following detailed description, reference is made to the accompanying drawings that form a part hereof. The drawings show by way of illustration how one or more embodiments of the disclosure may be practiced. These embodiments are described in sufficient detail to enable those of ordinary skill in the art to practice one or more embodiments of this disclosure. It is to be understood that other embodiments may be utilized and that process, electrical, and/or structural changes may be made without departing from the scope of the present disclosure.

As will be appreciated, elements shown in the various embodiments herein can be added, exchanged, combined, and/or eliminated so as to provide a number of additional embodiments of the present disclosure. The proportion and the relative scale of the elements provided in the figures are intended to illustrate the embodiments of the present disclosure, and should not be taken in a limiting sense.

As used herein, “a” or “a number of” something can refer to one or more such things. For example, “a number of voltage samples” can refer to one or more voltage samples.

FIG. 1 illustrates a system 100 for occupancy detection in accordance with one or more embodiments of the present disclosure. System 100 includes data acquisition subsystem 102 communicatively coupled to structure 110 and occupancy determination subsystem 104. For example, data acquisition subsystem 102 can be linked to structure 110 and/or occupancy determination subsystem 104 via, for example, the Internet or another wired and/or wireless connection.

As shown in FIG. 1, occupancy determination subsystem 104 can be a computing device having a processor and a memory (e.g., processor 108 and memory 106). Memory 106 can be volatile or nonvolatile memory. Memory 106 can also be removable (e.g., portable) memory, or non-removable (e.g., internal) memory. For example, memory 106 can be random access memory (RAM) (e.g., dynamic random access memory (DRAM), and/or phase change random access memory (PCRAM)), read-only memory (ROM) (e.g., electrically erasable programmable read-only memory (EEPROM), and/or compact-disk read-only memory (CD-ROM)), flash memory, a laser disk, a digital versatile disk (DVD), and/or other optical disk storage), and/or a magnetic medium such as magnetic cassettes, tapes, or disks, among other types of memory.

Further, although memory 106 is illustrated as being located in occupancy determination subsystem 104, embodiments of the present disclosure are not so limited. For example, memory 106 can also be located internal to another computing resource (e.g., enabling computer readable instructions to be downloaded over the Internet or another wired or wireless connection and/or network).

Memory 106 can store executable instructions, such as, for example computer readable instructions (e.g., software), for occupancy detection in accordance with one or more embodiments of the present disclosure. Memory can also store date, for example, data used by executable instructions to perform one or more functions or stored for analysis, among other purposes. For example, processor 108 can execute the executable instructions stored in memory 106 to perform one or more of the methods for detecting occupancy further described herein (e.g., in connection with FIG. 2).

Structure 110 can be any structure that can accommodate one or more occupants (e.g., individuals). For example, structure 110 can be a house, an office building, an apartment complex, and/or a hospital, among other types of structures. Structure 110 can also refer to subsets of larger structures. For example, structure 110 can include a room, an office, a wing, a walk-in freezer, and/or a hallway, as well as combinations and/or portions of these examples and/or others. Embodiments of the present disclosure do not limit structure 110 to man-made structures; rather, structure 110 can be any enclosed (e.g., partially enclosed) area that has a capability to accommodate one or more occupants.

In some embodiments, data acquisition subsystem 102 can include a computing device having a processor and a memory in a manner such as in the computing device discussed with reference to occupancy determination subsystem 104. Data acquisition subsystem 102 and occupancy determination subsystem 104 can, in some embodiments be part of the same computing device. Data acquisition subsystem 102 can be utilized to determine (e.g., acquire) energy consumption data associated with a structure (e.g., structure 110) at a point in time. Point in time data can be, for example, one or more periods of time, one or more instants in time, and/or combinations thereof.

Energy consumption data can include, for example, voltage data, current data, frequency data, and/or combinations thereof, including other kinds of data associated with the energy consumption of the structure 110. Data acquisition subsystem 102 can acquire energy consumption data from, for example, an electric service panel of structure 110. For instance, data acquisition subsystem 102 can acquire energy consumption data from a current and/or voltage sensor network, an energy meter, and/or a smart AMI (Advanced Metering Infrastructure) meter, among other power sensing devices, and/or combinations thereof.

To acquire energy consumption data, a current drawn and/or a total load from structure 110 may have to be stepped down, in some instances, into a range suitable for measurement and/or analysis by, for example, occupancy determination subsystem 104. For example, system 100 can include a number of current sensor coils and/or a current transformer which can step down the total current drawn and/or total load.

Data acquisition subsystem 102 can acquire energy consumption data associated with structure 110 at a predetermined time. Additionally and/or alternatively, data acquisition subsystem 102 can acquire additional energy consumption data associated with structure 110 at a predetermined interval and/or at random times.

In some embodiments, data acquisition subsystem 102 can acquire energy consumption data from an electrical supply associated with structure 110. The electrical supply can include, for example, a two phase, three wire system with a 60 Hertz (Hz) frequency and a phase voltage (V) of 120 V, a three phase, four wire, 50 Hz electric distribution system with a phase voltage of 230 V, a single phase 60 Hz system with a phase voltage of 120 V, and/or a single phase, 50 Hz, 230 V power supply. However, embodiments of the present disclosure are not limited to acquiring energy consumption data from the example power supplies listed above; rather, system 100 can acquire energy consumption data from combinations of the example sources listed above, and/or from any suitable source, such as, for example, an electricity provider.

Data acquisition subsystem 102 can acquire the energy consumption data by sampling analog voltage and/or current signals received from a source (e.g., a voltage and current sensor network associated with structure 110) at a desired frequency. Further, data acquisition subsystem 102 can adjust the sampling frequency based on the processing capability (e.g., processing capability of processor 108) of occupancy determination subsystem 104. For example, data acquisition subsystem 102 can acquire a number of voltage and current samples from structure 110 at a predetermined frequency, process the raw samples, and/or modify the sampling rate for communication to the occupancy determination subsystem 104.

Once the energy consumption data has been acquired by the data acquisition subsystem 102, data acquisition subsystem 102 can communicate (e.g., send) the energy consumption data to occupancy determination subsystem 104. That is, occupancy determination subsystem 104 can receive the energy consumption data from data acquisition subsystem 102. From the energy consumption data (e.g., voltage data, current data, frequency data, and/or combinations thereof, including other kinds of data), occupancy determination subsystem 104 can determine additional (e.g., secondary) energy consumption data. Additional energy consumption data can include, by way of example and not limitation, active power, reactive power, apparent power, electrical energy, kilowatt hours (KWh), line frequency, peak current, rate of change of current, harmonic content, peak power, Root Mean Square (RMS) power, and/or combinations thereof, including other additional energy data associated with the energy consumption of structure 110.

Occupancy determination subsystem 104 can determine whether structure 110 is occupied at the point in time based, at least in part, on the energy consumption data. For example, occupancy determination subsystem 104 can determine whether structure 110 is occupied at the point in time based, at least in part, on the energy consumption data acquired by data acquisition subsystem 102 and/or the additional energy consumption data determined by occupancy determination subsystem 104.

As an example, occupancy determination subsystem 104 can determine whether the structure is occupied at the point in time by determining whether a first portion of the energy consumption data meets or exceeds a threshold for the first portion of the energy consumption data. The threshold can, for example, be stored in the memory 106.

Occupancy determination subsystem 104 can determine whether the first portion of the energy consumption data meets or exceeds the threshold by comparing the first portion of the energy consumption data with historical energy consumption data. For example, occupancy determination subsystem 104 can acquire peak current, RMS current and/or rate of change of current, among other energy consumption data, and compare these energy consumption data to historical energy consumption data including, for example, historical peak current, historical RMS current, and/or historical rate of change of current, among others.

If the first portion of the energy consumption data meets or exceeds the threshold, it may exhibit characteristics that indicate occupancy based on the historical data. For example, energy consumption data can include a number of power spikes over a period of time, and system 100 (e.g., occupancy determination subsystem 104) can consider power level as an indicator in determining whether structure 110 is occupied. However, embodiments of the present disclosure do not limit occupancy detection to observation of power levels; rather, as discussed below, a number of systems in structure 110 may affect power level without a correlation to occupancy.

For example, a portion of a number of power spikes for a time period can indicate only that an automated appliance (e.g., a refrigerator and/or a heating system) is turning on or off. An automated appliance turning on or off may not indicate occupancy, because, for example, this behavior can occur in the presence of one or more occupants, or alternatively, in the absence of any occupants. In this regard, occupancy determination subsystem 104 can, for example, consider power levels as an indicator of occupancy, rather than a determining factor.

Additionally, occupancy determination subsystem 104 can use rate of change of power level data (e.g., frequency of power level change) as an indicator of occupancy. For example, in structure 110 without occupant(s), power levels can change (e.g., peak and/or dip) due to, for example, automated system behavior as previously discussed. Conversely, structure 110 having one or more occupants can exhibit a different power signature.

For example, if an occupant is present in structure 110, the occupant can turn lights on and/or off, and/or activate a user-activated appliance, such as, for example, a microwave, a toaster oven, a television set, a radio, etc., among other occupant-initiated activities. Moreover, the occupant can, for example, engage in these powered-level altering activities with a frequency not likely to be equaled in automated system behavior. In this regard, occupancy determination subsystem 104 can, for example, use a rate of change of power level in determining whether structure 110 is occupied.

Occupancy determination subsystem 104 can determine whether the first portion of the energy consumption data meets or exceeds the threshold for the first portion of the energy consumption data by, for example, comparing and/or weighing multiple aspects of energy consumption data. For example, when a Heating, Ventilating, and Air Conditioning (HVAC) system is turned on, it can produce a large change in a power level of a structure. Conversely, an occupant activating a light switch and a television as the occupant enters a room may produce a substantially smaller change in a power level of the structure than the HVAC system.

While the power level change can, for example, be greater for the HVAC system turning on than for the light and the television, the HVAC system may have a lesser determinative effect regarding occupancy than the activation of the light followed shortly thereafter by the activation of the television due to, for example, the difference in frequency of the power level changes. That is, occupancy determination subsystem 104 can compare power level and frequency, among other aspects of energy consumption data, to determine whether the first portion of the energy consumption data meets or exceeds the threshold.

Occupancy determination subsystem 104 can separate a particular aspect of the energy consumption data that may not indicate occupancy (e.g., is not associated with an occupancy state). For example, occupancy determination subsystem 104 can detect (e.g., determine) an energy signature from an HVAC system in normal operation by analyzing, for example, historical data associated with the HVAC system.

Continuing in the example, occupancy determination subsystem 104 can remove the energy signature of the normally-operating HVAC system from the energy consumption data used by occupancy determination subsystem 104 to determine occupancy. Embodiments of the present disclosure do not limit occupancy determination subsystem 104 to removal of HVAC energy consumption data; rather, occupancy determination subsystem 104 can remove (e.g., successively remove) energy consumption data from any source that can be determined (e.g., determined by occupancy determination subsystem 104) to have little and/or no bearing on a determination of occupancy.

Removing aspects of the energy consumption data can allow occupancy determination subsystem 104 to determine occupancy by analyzing aspects of the energy consumption data more likely to indicate occupancy and/or non-occupancy. For example, determining occupancy based on a portion of the energy consumption data associated with an occupancy state (e.g., occupied or not occupied) can allow occupancy determination subsystem 104 to disregard other portions of the energy consumption data that do not indicate occupancy and/or non-occupancy.

Occupancy determination subsystem 104 can receive energy consumption data that has been pre-separated. For example, data acquisition subsystem 102 can be communicatively coupled to multiple energy consumption meters.

An energy consumption meter, as previously discussed, can be associated with (e.g., communicatively coupled to) structure 110. Embodiments of the present disclosure, however, do not limit energy consumption meters to one per structure.

Energy consumption meters can be communicatively coupled to an area (e.g., room) within structure 110, an appliance within structure 110, and/or grouping of appliances within structure 110, among others. For example, an energy consumption meter can be associated with a microwave oven, a spa, a basement, a lamp, a set of lamps, a section of offices, etc. The energy consumption meter(s) associated with structure 110 can communicate energy consumption data of these devices, areas, and/or subsystems to data acquisition subsystem 102 in combination and/or individually.

Data acquisition subsystem 102 can communicate the energy consumption data to occupancy determination subsystem 104, as previously discussed. Communications between structure 110 and occupancy determination subsystem 104 can be associated with one or more identifiers to indicate an origin (e.g., room, appliance, etc.) of the energy consumption data.

Alternatively and/or additionally, occupancy determination subsystem 104 can receive undivided energy consumption data (e.g., not pre-separated) from structure 110. Occupancy determination subsystem 104 can then identify aspects (e.g., portions) of the energy consumption data and selectively remove aspects that do not indicate occupancy and/or non-occupancy.

For example, a spa may have a predictable signature (e.g., frequency) of “off” and “on” modes. Occupancy determination subsystem 104 can determine that this signature is not useful for occupancy detection and can, for example, disregard and/or filter the signature out.

Embodiments of the present disclosure are not limited to using power level and rate of change of power level to determine whether a first portion of the energy consumption data meets or exceeds the threshold for the first portion of the determined energy consumption data. Rather, as previously discussed, other energy consumption data may be considered, including but not limited to: active power, reactive power, apparent power, electrical energy, kilowatt hours (KWh), line frequency, peak current, rate of change of current, harmonic content, peak power, Root Mean Square (RMS) power, and/or combinations of these energy data and/or other energy data.

Occupancy determination subsystem 104 can determine whether the first portion of the energy consumption data meets or exceeds the threshold for the first portion of the energy consumption data using, for example, a short-time-Fourier transform (STFT)-based spectrum estimation to determine a sinusoidal frequency and a phase content of local sections of the power consumption data as it changes over a time period. Alternatively and/or additionally, other methods may be used, including but not limited to: a non-parametric method, an adaptive filtering algorithm, a Kalman filter, a wavelet based time-frequency estimation method, a wavelet transform based method, and/or combinations of these methods and/or other methods.

For example, a wavelet transform based method can include determining a number of distinct wavelet signatures and removing power spikes (e.g., power spikes caused by activation of HVAC system) by the use of, for example, a peak detector. If the first portion of the energy consumption data meets or exceeds the first threshold, occupancy determination subsystem 104 can determine whether a second portion of the energy consumption data meets or exceeds a threshold for the second portion of the energy consumption data. The threshold can be, for example, stored in memory 106.

For example, occupancy determination subsystem 104 can analyze historical energy consumption data along with energy consumption data received from data acquisition subsystem 102 (e.g., energy consumption data newly received from data acquisition subsystem 102). Historical data used to determine whether the second portion of the energy consumption data meets or exceeds the threshold can include, for example, historical data analogous to that previously discussed.

Occupancy determination subsystem 104 can, for example, determine a number of occupancy trends, based at least in part on, for example, a particular time of a day, a particular day of a week, a particular week of a year, a particular season of a year, etc., and/or combinations of these. For instance, an individual may work during the day Monday through Friday, and return to his house (e.g., structure 110) at substantially the same time in the evening (e.g., 6:00 pm).

Occupancy determination subsystem 104 can use this historical data in the determination regarding occupancy detection, among other uses. For example, a small power spike within structure 110 that may not otherwise indicate occupancy may be given more weight if it occurs, for example, at 6:15 pm on a weekday).

As an additional example, the individual may travel during the same week in August each year. Occupancy determination subsystem 104 can, for example, use this historical data to give less weight to energy consumption data indicating occupancy during that week.

As another example, the individual may keep longer hours outside of structure 110 during the summer months, especially on Saturdays. Occupancy determination subsystem 104 can, for example, use this information to give less weight to energy consumption data indicating occupancy at, for example, noon on a July Saturday.

Occupancy determination subsystem 104 can determine that structure 110 is occupied if the second portion for the energy consumption data meets or exceeds the second threshold. Meeting or exceeding the second threshold can include, for example, the determined energy consumption data indicating a threshold-level of any of the examples of the energy consumption data as previously discussed, and/or combinations of these examples and/or other energy consumption data, as previously discussed, with comparison to the historical energy consumption data (e.g., any and/or all of the previously discussed examples of energy consumption data, among others).

Occupancy determination subsystem 104 can determine that structure 110 is not occupied if the second portion for the energy consumption data does not meet or exceed the second threshold with comparison to the historical energy consumption data (e.g., any and/or all of the previously discussed examples of the historical energy consumption data, among others). By comparing historical occupancy and/or energy consumption data stored in the memory (e.g., memory 106), occupancy determination subsystem 104 can, for example, arrive at a final conclusion regarding occupancy within structure 110. Historical energy consumption data combined with data acquired from structure 110 by data acquisition subsystem 102 allows occupancy determination subsystem 104 to detect occupancy, for example, more accurately than by considering historical energy consumption data or energy from data acquisition subsystem 102 (e.g., newly-received energy consumption data) by itself.

In some embodiments, occupancy determination subsystem 104 can determine whether structure 110 is occupied at the point in time based, at least in part, on an input from a user at a security system (e.g., security system 114 shown in FIG. 1) associated with the structure, in addition to and/or in spite of energy consumption data acquired by data acquisition subsystem 102 and/or historical energy consumption data stored in the memory 106. That is, occupancy determination subsystem 104 can receive an input (e.g., feedback) from a user that can include a direct indication of occupancy and/or non-occupancy. For example, if an individual has a security system (e.g., security system 114 shown in FIG. 1) installed in structure 110, the individual can indicate that the structure is not occupied by activating security system 114 by, for example, entering a code.

Further, the user can indicate that the structure is occupied by deactivating the security system by, for example, entering a code. An input into security system 114 can be used by occupancy determination subsystem 104 as a direct indication of occupancy or non-occupancy, and/or can be used in addition to energy consumption data acquired from structure 110 by data acquisition subsystem 102.

Occupancy determination subsystem 104 can determine whether a structure (e.g., structure 110) is occupied at a point in time based, at least in part, on an input from an occupancy sensing device (e.g. occupancy sensor 116 shown in FIG. 1), and/or a number of occupancy sensing devices working alone or in combination. Occupancy sensing devices, can be used in conjunction with energy consumption data acquired by data acquisition subsystem 102, and include many possible devices, such as, for example, acoustic devices, cameras, infrared (IR) sensing devices, carbon dioxide (CO₂) detectors, etc.

Although shown as separated in FIG. 2, security system 114 and occupancy sensor 116 can be included within the same system and/or computing device. For example, security system 114 can include occupancy sensor 116.

If a structure 110 is determined to be occupied, system 100 (e.g., occupancy determination subsystem 104) can send a communication to a user device 112. User device 112 can be, for example, a computing device and/or a mobile device (e.g., a cellular telephone and/or a personal digital assistant (PDA)), among other devices. Communications sent to user device 112 can, for example, indicate that occupancy has been determined within structure 110. As an example, a user may seek heightened security for structure 110 and may be interested in being notified if structure 110 is occupied.

Occupancy determination subsystem 104 can send, for example, an email, a text message, a voice message, and/or a social media message, among other types of electronic communications, to inform the user about the occupancy status of structure 110. Upon receiving such a message, the user can then input, via user device 112, a verification and/or command to occupancy determination subsystem 104.

For example, if the user is out of the country and receives a text message informing the user that structure 110 is determined to be occupied, the user can, for example, make a determination that structure 110 should not be occupied. The user can then inform occupancy determination subsystem 104, via user device 112, about an unauthorized occupancy of structure 110. Additionally and/or alternatively, the user can notify security system 114 directly and/or via occupancy determination subsystem 104.

Embodiments of the present disclosure do not limit communications sent by occupancy determination subsystem 104 to user device 112 only to instances where occupancy is detected. Rather, a communication can be sent by occupancy determination subsystem 104 to user device 112 in a number of scenarios, including, for example, upon a determination that structure 110 is not occupied.

If structure 110 is determined to be occupied, system 100 (e.g., occupancy determination subsystem 104) can send a communication to a programmable thermostat. Communications between a programmable thermostat and system 100 are not limited to situations where a structure (e.g., structure 110) is deemed to be occupied; rather, system 100 and/or occupancy determination subsystem 104 can send communications to a programmable thermostat in cases where a structure (e.g., structure 110) is not occupied.

Sending a communication to a programmable thermostat can include, for example, a communication to activate or deactivate an HVAC system, and/or to adjust a number of set points of the programmable thermostat. For example, if structure 110 is determined to not be occupied, occupancy determination subsystem 104 can send that determination to a programmable thermostat which can then, for example, adjust heating and/or cooling settings for the structure 110 (e.g., a portion of structure 110 controlled by the programmable thermostat).

One or more embodiments according to the present disclosure can allow occupancy determination subsystem 104 to predict a number of behaviors, including, for example, a return time of a user to structure 110. For example, user device 112 can be equipped with Global Positioning System (GPS) technology through which occupancy determination subsystem 104 can receive a location of user device 112.

The location of a user device 112 can be used by occupancy determination subsystem 104 to determine occupancy. For instance, if a user typically leaves work on a weekday at approximately 5:30 pm and drives directly to structure 110, occupancy determination subsystem 104 can compare a GPS location of the user (e.g., the user carrying user device 112) with a detected occupancy state within structure 110. Occupancy determination subsystem 104 can, for example, use this comparison to predict a return time of the user to structure 110, and can additionally, for example, make a more accurate determination regarding occupancy and/or non-occupancy at a point in time.

Occupancy determination subsystem 104 can use additional methods to make a prediction regarding a determination of occupancy. For example, user device 112 can have an electronic calendar associated with it. A user of user device 112 can use the calendar to program the user's schedule (e.g., meeting times, and/or locations, among other schedule data).

Occupancy determination subsystem 104 can use this calendar data to determine an occupancy state within structure 110. For example, if a user has a meeting at a particular time at a location not within structure 110, occupancy determination subsystem 104 can use this data to determine whether an occupant is present in structure 110.

FIG. 2 illustrates a method 220 for occupancy detection in accordance with one or more embodiments of the present disclosure. Method 220 can be performed, for example, by system 100 (e.g., occupancy determination subsystem 104) previously described in connection with FIG. 1 to determine whether a structure is occupied at a point in time based, at least in part, on energy consumption data associated with the structure.

At block 230, method 220 includes determining energy consumption data associated with a structure (e.g., structure 110 previously described in connection with FIG. 1) at a point in time. Energy consumption data can include, for example, energy consumption data analogous to that previously discussed in connection with FIG. 1. A point in time and structure can include, for example, a point in time and structure analogous to those previously discussed in connection with FIG. 1.

At block 232, method 220 includes determining whether an anomaly exists within the energy consumption data by comparing a first portion of the energy consumption data to a historical threshold for the first portion of the energy consumption data if the first portion of the energy consumption data meets or exceeds the historical threshold. An anomaly exists within the energy consumption data if the first portion of the energy consumption data meets or exceeds the historical threshold. Comparing a first portion of the energy consumption data to a historical threshold for the first portion for the energy consumption data can be analogous to the determination of whether a first portion of the determined energy consumption data meets or exceeds a threshold for the first portion of the determined energy consumption data stored in the memory in a manner analogous to that previously discussed in connection with FIG. 1.

At block 234, method 220 includes determining, if an anomaly exists within the energy consumption data, whether the anomaly is an anomaly of interest by comparing a second portion of the energy consumption data to a historical threshold for the second portion of the energy consumption data. The anomaly is an anomaly of interest if the second portion of the energy consumption data meets or exceeds the historical threshold for the second portion of the energy consumption data. Determining whether the anomaly is an anomaly of interest can be analogous to the determination of whether a second portion of determined energy consumption data meets or exceeds a threshold for the second portion of the determined energy consumption data previously discussed in connection with FIG. 1.

At block 236, method 220 includes determining whether the structure (e.g., structure 110) is occupied at the point in time based, at least in part, on whether the anomaly is an anomaly of interest. The structure is occupied at the point in time if the anomaly is an anomaly of interest, and the structure is not occupied at the point in time if the anomaly is not an anomaly of interest. Determining whether the structure is occupied at the point in time based, at least in part, on whether the anomaly is an anomaly of interest can be analogous to the determination of whether the structure is occupied at the point in time based, at least in part, on determined (e.g., acquired) energy consumption data previously discussed in connection with FIG. 1.

Although not shown in FIG. 2, method 220 can include determining whether structure 110 is occupied at the point in time based, at least in part, on feedback received from a user in a manner analogous to that previously discussed in connection with FIG. 1, on GPS data associated with a user in a manner analogous to that previously discussed in connection with FIG. 1, and/or on a particular day of a week, on a particular season of a year and/or combinations thereof and/or other time indicators in a manner analogous to that previously discussed in connection with FIG. 1.

Although specific embodiments have been illustrated and described herein, those of ordinary skill in the art will appreciate that any arrangement calculated to achieve the same techniques can be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments of the disclosure.

It is to be understood that the above description has been made in an illustrative fashion, and not a restrictive one. Combination of the above embodiments, and other embodiments not specifically described herein will be apparent to those of skill in the art upon reviewing the above description.

The scope of the various embodiments of the disclosure includes any other applications in which the above structures and methods are used. Therefore, the scope of various embodiments of the disclosure should be determined with reference to the appended claims, along with the full range of equivalents to which such claims are entitled.

In the foregoing Detailed Description, various features are grouped together in example embodiments illustrated in the figures for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the embodiments of the disclosure require more features than are expressly recited in each claim.

Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment. 

What is claimed is:
 1. An HVAC controller for controlling an HVAC system of a structure, the HVAC controller comprising: a memory storing executable instructions that, when executed, compare energy consumption data to one or more historical energy consumption thresholds to determine an occupancy status of the structure, and when the occupancy status is indicated as being occupied, adjust an HVAC setting to a comfort value, and when the occupancy status is indicated as being unoccupied, adjust the HVAC setting to an energy savings value; a controller coupled to the memory, wherein the controller is configured to execute the executable instructions stored in the memory to: obtain energy consumption data associated with the structure; determine the occupancy status of the structure by comparing the energy consumption data to the one or more historical energy consumption thresholds stored in the memory; when the determined occupancy status is indicated as being occupied, adjust the HVAC setting of the HVAC system servicing the structure to the comfort value; when the determined occupancy status is indicated as being unoccupied, adjust the HVAC setting of the HVAC system servicing the structure to the energy savings value; and thermostatically control the HVAC system servicing the structure in accordance with the comfort value when the adjusted HVAC setting is set to the comfort value, and thermostatically control the HVAC system in accordance with the energy savings value when the adjusted HVAC setting is set to the energy savings setting.
 2. The HVAC controller of claim 1, wherein the HVAC setting comprises a heating setting.
 3. The HVAC controller of claim 1, wherein the HVAC setting comprises a cooling setting.
 4. The HVAC controller of claim 1, wherein the controller is further configured to report the occupancy status to a security system that services the building.
 5. The HVAC controller of claim 1, wherein the controller is further configured to report the occupancy status to another device.
 6. The HVAC controller of claim 1, wherein the controller is further configured to report the occupancy status to a user device via an electronic message.
 7. The HVAC controller of claim 1, wherein the controller is configured to determine the occupancy status of the structure based, at least in part, on feedback received from a user.
 8. The HVAC controller of claim 1, wherein the controller is configured to determine the occupancy status of the structure based, at least in part, on GPS data associated with a user.
 9. The HVAC controller of claim 1, wherein the GPS data associated with the user comprises: a GPS location of the user; and a predicted return time of the user to the structure determined based at least in part on the GPS location of the user.
 10. The HVAC controller of claim 1, wherein the controller is configured to determine the occupancy status of the structure based, at least in part, on a particular day of a week.
 11. The HVAC controller of claim 1, wherein the controller is configured to determine the occupancy status of the structure based, at least in part, on a particular season of a year.
 12. The HVAC controller of claim 1, wherein the comparison of the energy consumption data to the one or more historical energy consumption thresholds comprises comparing a first portion of the energy consumption data to a historical threshold for the first portion of the energy consumption data, and when the first portion of the energy consumption data meets or exceeds the historical threshold for the first portion of the energy consumption data, comparing a second portion of the energy consumption data to a historical threshold for the second portion of the energy consumption data.
 13. The HVAC controller of claim 6, wherein the electronic message is one or more of an email message, a text message, a voice message, and a social media message.
 14. The HVAC controller of claim 12, wherein: the first portion of the energy consumption data comprises one or more of a peak current, an RMS current, a rate of change of current, and a frequency of power level changes; and the historical threshold for the first portion comprises one or more of a corresponding historical peak current, a historical RMS current, a historical rate of change of current, and a historical frequency of power level changes.
 15. The HVAC controller of claim 12, wherein the controller is configured to determine when the first portion of the energy consumption data meets or exceeds the historical threshold for the first portion of the energy consumption data via one or more of: (1) short-time-Fourier transform (STFT)-based spectrum estimation to determine a sinusoidal frequency and a phase content of local sections of the energy consumption data as it changes over a time period; (2) a non-parametric algorithm; (3) an adaptive filtering algorithm; (4) a Kalman filter; (5) a wavelet based time-frequency estimation algorithm; and (6) a wavelet transform based algorithm.
 16. The HVAC controller of claim 12, wherein the controller is configured to determine when the second portion of the energy consumption data meets or exceeds the historical threshold for the second portion of the energy consumption data using one or more of: (1) short-time-Fourier transform (STFT)-based spectrum estimation to determine a sinusoidal frequency and a phase content of local sections of the energy consumption data as it changes over a time period; (2) a non-parametric algorithm; (3) an adaptive filtering algorithm; (4) a Kalman filter; (5) a wavelet based time-frequency estimation algorithm; and (6) a wavelet transform based algorithm. 